Solving Perception Challenges for Autonomous Vehicles Using SGD

All levels of autonomy in vehicles faces a critical problem due to the fragility and lack of robustness of state of the art image classifiers to perturbations in the input image. Specifically, it has been repeatedly shown that classifiers that enjoy extremely high accuracy on test sets and challenge sets, are remarkably susceptible to misclassifying images that have small, but planted, perturbations. Stop signs can be misclassified as yield signs, with modifications that are imperceptible to the casual human observer.


  • English


  • Status: Active
  • Contract Numbers:


  • Sponsor Organizations:

    Department of Transportation

    Intelligent Transportation Systems Joint Program Office
    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590
  • Project Managers:

    Bhat, Chandra

  • Performing Organizations:

    Data-Supported Transportation Operations and Planning Center

    University of Texas at Austin
    Austin, TX  United States  78701
  • Principal Investigators:

    Caramanis, Constantine

  • Start Date: 20180901
  • Expected Completion Date: 20200831
  • Actual Completion Date: 0
  • Source Data: 159

Subject/Index Terms

Filing Info

  • Accession Number: 01670181
  • Record Type: Research project
  • Source Agency: Data-Supported Transportation Operations and Planning Center
  • Contract Numbers: DTRT13-G-UTC58
  • Files: UTC, RIP
  • Created Date: May 23 2018 4:49PM